Various search methods of searching for a desired image from a database storing a large amount of image data have been proposed. These search methods are roughly classified into:                methods of relating non-image information such as a keyword or the date/time of photography to images, and searching for an image on the basis of the information; and        methods of searching for an image on the basis of the image feature amount (e.g., luminance/color difference information, image frequency, or histogram) of that image.        
In the latter methods, a method of searching for an image by presenting a certain image to a database and using the image feature amount of that image as a search condition is especially called similar image search. This method has the advantage that an intuitively readily understandable search interface can be provided to a user who has no particular knowledge about image processing.
Unfortunately, the conventional similar image search poses the following problems when calculating an image feature amount by segmenting an image into several regions and averaging pixel values contained in each region as the image feature amount of that image.                If the number of segmented regions is large, the calculation is time-consuming, and no good search result can be obtained if the image position deviates.        If the number of segmented regions is small, not enough image feature can be detected, so no good search result can be obtained.        